stepup: Stepup model search along modification indices

Description Usage Arguments Value Author(s) See Also Examples

View source: R/d_stepup.R

Description

This function automatically peforms step-up search by adding the parameter with the largest modification index until some criterion is reached or no modification indices are significant at alpha.

Usage

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stepup(x, alpha = 0.01, criterion = "bic", matrices, mi =
                    c("mi", "mi_free", "mi_equal"), greedyadjust =
                    c("bonferroni", "none", "holm", "hochberg", "hommel",
                    "fdr", "BH", "BY"), stopif, greedy = FALSE, verbose,
                    checkinformation = TRUE, singularinformation =
                    c("tryfix", "skip", "continue", "stop"), startEPC =
                    TRUE, ...)

Arguments

x

A psychonetrics model.

alpha

Significance level to use.

criterion

String indicating the criterion to minimize. Any criterion from fit can be used.

matrices

Vector of strings indicating which matrices should be searched. Will default to network structures and factor loadings.

mi

String indicating which kind of modification index should be used ("mi" is the typical MI, "mi_free" is the modification index free from equality constrains across groups, and "mi_equal" is the modification index if the parameter is added constrained equal across all groups).

greedyadjust

String indicating which p-value adjustment should be used in greedy start. Any method from p.adjust can be used.

stopif

An R expression, using objects from fit, which will break stepup search if it evaluates to TRUE. For example, stopif = rmsea < 0.05 will lead to search to stop if rmsea is below 0.05.

greedy

Logical, should a greedy start be used? If TRUE, the first step adds any parameter that is significant (after adjustement)

verbose

Logical, should messages be printed?

checkinformation

Logical, should the Fisher information be checked for potentially non-identified models?

singularinformation

String indicating how to proceed if the information matrix is singular. "tryfix" will adjust starting values to try to fix the proble, "skip" will lead to the algorithm to skip the current parameter, "continue" will ignore the situation, and "stop" will break the algorithm and return a list with the last two models.

startEPC

Logical, should the starting value be set at the expected parameter change?

...

Arguments sent to runmodel

Value

An object of the class psychonetrics (psychonetrics-class)

Author(s)

Sacha Epskamp

See Also

prune

Examples

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# Load bfi data from psych package:
library("psychTools")
data(bfi)

# Also load dplyr for the pipe operator:
library("dplyr")

# Let's take the agreeableness items, and gender:
ConsData <- bfi %>%
  select(A1:A5, gender) %>%
  na.omit # Let's remove missingness (otherwise use Estimator = "FIML)

# Define variables:
vars <- names(ConsData)[1:5]

# Let's fit a full GGM:
mod <- ggm(ConsData, vars = vars, omega = "full")

# Run model:
mod <- mod %>%runmodel %>%prune(alpha = 0.05)

# Remove an edge (example):
mod <- mod %>%fixpar("omega",1,2) %>%runmodel

# Stepup search
mod <- mod %>%stepup(alpha = 0.05)

psychonetrics documentation built on Oct. 26, 2021, 1:06 a.m.